The thalamus plays a key role in integrating sensory information for further processing in the basal ganglia and cortex. In multiple sclerosis (MS), long thought to be primarily a white matter disease, it has recently been shown that cognitive decline is more strongly related to thalamic volume than to white matter magnetic resonance image (MRI) lesion load. Since the thalamus is made up of nuclei having specific physical connections within the brain, it may be possible to relate physical changes in thalamic nuclei caused by MS to specific cognitive, behavioral, or disease subtype differences. This grant proposes to develop an automated method and associated software tool to carry out thalamic nuclei parcellation using MRI. Specifically, it is proposed to: 1) optimize the computation of thalamic features from anatomical and diffusion MRI; 2) develop an integrated, multi-nuclear thalamus segmentation algorithm; 3) optimize the algorithm parameters using manual delineations; and 4) carry out a pilot study using an existing MRI database comprising 99 normal controls and 226 MS patients. The work builds on previous methods that exploit topology and connectivity in order to improve segmentation robustness. The primary innovation is to provide a coordinated multi-object approach that integrates intensity information from T1-weighted MRI with orientation information and connectivity information obtained from diffusion MRI. Primary diffusion directions will be mapped to a five- dimensional space in order to cluster nuclei by diffusion orientation and use this information in the parcellation algorithm. A machine learning approach applied to manual delineations will be used to learn boundary-specific properties that will be used to carry out a joint parcellation approach. The algorithm will be designed for conventional three tesla clinical MRI and will be validated using high-resolution, high signal-to-noise ratio seven tesla MRI on 15 subjects scanned contemporaneously with their three tesla scans. The pilot study will use 822 scans of 305 participants, and will examine longitudinal stability of the algorithm and a cross-sectional univariate statistical analysis relatng thalamic nuclei (or nuclear groups) volumes to various clinical measures including disease subtype, disease duration, visual acuity, and two standard MS composite disability scores. An exploratory principal component analysis of multiple thalamic nuclear volumes will be carried out to look for patterns of atrophy and their relationships to various clinical measures. The algorithm will be made publicly available as open source code on the NITRC website so that the entire neuroscience community will be able to use the algorithm to study other diseases or modify and extend it for other applications.